With the proliferations of new types of communications devices, including smart phones, tablets, laptop computers and multimedia gateways for example, as well as more traditional devices, such as set-top boxes, consumers now make use (e.g., consume) ever increasing quantities of content (e.g., games, books, applications, music, images, movies and/or videos). To that end, content providers, such as movie studios, game developers, broadcast networks, and publishers for example, have a strong interest in recommending their content to consumers for consumption. Present day content recommendations techniques typically make recommendations based on the content previously consumed by a particular consumer. Thus, if a consumer has previously viewed a historical drama, a movie studio would likely recommend other historical dramas for viewing. Similarly, a consumer who has previously ordered video games of a certain type thus constitutes a good candidate for similar games of the same type.
Some content recommendation techniques also take account of the nature of the device through which the consumer makes a request for content. Some devices have greater capabilities than other devices. For example, a High-Definition (HD) display device or HD set-top box can handle HD movies whereas an unsophisticated cell phone cannot readily do so. Thus, knowing the capability of the device enables a content recommendation appropriate for the device to avoid potential consumer dissatisfaction. However, such content recommendation techniques assume that the user will consume content on the same device through which the user made the content request. However, consumers do not always use the same device for requesting and consuming content. In some instances, the consumer can use one type of device, such as a cell phone or a tablet, to make a content request, but use a different device, such as a HD display device to view such content. If the content provider making the content recommendation assumes the same device in both instances, the content provider will miss the opportunity to recommend a wider variety of content, and thus miss the opportunity to gain greater revenue from the consumer.
Thus, a need exists for a content recommendation technique that takes account of various factors that influence consumer content buying decisions.